Testing of precision agricultural networks for adversary-induced problems

Authors:
Karel P. Bergmann;Jörg Denzinger
Affiliations:
University of Calgary, Calgary, AB, Canada;University of Calgary, Calgary, AB, Canada
Venue:
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Year:
2013

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Abstract

We present incremental adaptive corrective learning as a method to test ad-hoc wireless network protocols and applications. This learning method allows for the evolution of complex, variable-length, cooperative behaviour patterns for adversarial agents acting in such networks. We used the method to test precision agriculture sensor networks for vulnerabilities which could be exploited by attackers to significantly increase power consumption within the network. Our technique was able to find behaviours which increased power consumption by at least a factor of 3.6 for a node in each of the tested scenarios.